An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans

TL;DR

This paper introduces an ensemble deep learning model for COVID-19 severity prediction from chest CT scans, utilizing data augmentation and test-time augmentation to enhance accuracy, achieving competitive results in a challenge.

Contribution

The study presents a simple yet effective ensemble approach with strong test-time augmentation for COVID-19 severity prediction from CT scans, securing a top position in a public challenge.

Findings

Achieved fourth place in the STOIC COVID-19 AI Challenge.

Utilized data augmentation and test-time augmentation to improve model performance.

Developed an ensemble model that performs comparably to more complex methods.

Abstract

Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality. We present our findings on COVID-19 severity prediction from chest CT scans using the STOIC dataset. We developed an ensemble deep learning based model that incorporates multiple neural networks to improve predictions. To address data imbalance, we used slicing functions and data augmentation. We further improved performance using test time data augmentation. Our approach which employs a simple yet effective ensemble of deep learning-based models with strong test time augmentations, achieved results comparable to more complex methods and secured the fourth position in the STOIC2021 COVID-19 AI Challenge. Our code is available on online: at: https://github.com/aleemsidra/stoic2021- baseline-finalphase-main.